1. Field of the Invention
The present invention relates to video images, and more particularly to a system and method of enhancing video coding.
2. Description of the Background Art
Video images have become an increasingly important part of communications in general. The ability to nearly instantaneously transmit still images, and particularly, live moving images, has greatly enhanced global communications.
In particular, videoconferencing systems have become an increasingly important business communication tool. These systems facilitate meetings between persons or groups of persons situated remotely from each other, thus eliminating or substantially reducing the need for expensive and time-consuming business travel. Since videoconference participants are able to see facial expressions and gestures of remote participants, richer and more natural communication is engendered. In addition, videoconferencing allows sharing of visual information, such as photographs, charts, and figures, and may be integrated with personal computer applications to produce sophisticated multimedia presentations.
To provide cost-effective video communication, the bandwidth required to convey video must be limited. The typical bandwidth used for videoconferencing lies in the range of 128 to 1920 kilobits per second (Kbps). Problems associated with available videoconferencing systems as these systems attempt to cope with bandwidth limitations include slow frame rates, which result in a non-lifelike picture having an erratic, jerky motion; the use of small video frames or limited spatial resolution of a transmitted video frame; and a reduction in the signal-to-noise ratio of individual video frames. Conventionally, if one or more of these solutions is not employed, higher bandwidths are then required.
At 768 Kbps, digital videoconferencing, using state-of-the-art video encoding methods, produces a picture that may be likened to a scene from analog television. Typically, for most viewers, twenty-four frames per second (fps) are required to make video frames look fluid and give the impression that motion is continuous. As the frame rate is reduced below twenty-four fps, an erratic motion results. In addition, there is always a tradeoff between a video frame size required and available network capacity. Therefore, lower bandwidth requires a lower frame rate and/or reduced video frame size.
A standard video format used in videoconferencing, defined by resolution, is Common Intermediate Format (CIF). The primary CIF format is also known as Full CIF or FCIF. The International Telecommunications Union (ITU), based in Geneva, Switzerland (www.itu.ch), has established this communications standard. Additional standards with resolutions higher and lower than CIF have also been established. Resolution and bit rate requirements for various formats are shown in the table below. The bit rates (in megabits per second, Mbps) shown are for uncompressed color frames where 12 bits per pixel is assumed.
TABLE IResolution and bit-rates for various CIF formatsBit Rate at 30 fpsCIF FormatResolution (in pixels)(Mbps) SQCIF (Sub Quarter CIF)128  ×  96     4.4QCIF (Quarter CIF)176  ×  1449.1CIF (Full CIF, FCIF)352  ×  28836.54CIF (4  ×  CIF)704  ×  576146.016CIF (16 × CIF)1408 × 1152583.9
Video compression is a way of encoding digital video to take up less storage space and reduce required transmission bandwidth. Certain compression/decompression (CODEC) schemes are frequently used to compress video frames to reduce the required transmission bit rates. Overall, CODEC hardware or software compresses digital video into a smaller binary format than required by the original (i.e., uncompressed) digital video format.
H.263 is a document which describes a common contemporary CODEC scheme, requiring a bandwidth from 64 to 1920 Kbps. H.263 is an ITU standard for compressing video, and is generically known as a lossy compression method. Lossy coding assumes that some information can be discarded, which results in a controlled degradation of the decoded signal. The lossy coding method is designed to gradually degrade as a progressively lower bit rate is available for transmission. Thus, the use of lossy compression methods results in a loss of some of the original image information during the compression stage and, hence, the lost original image information becomes unrecoverable. For example, a solid blue background in a video scene can be compressed significantly with little degradation in apparent quality. However, other frames containing sparse amounts of continuous or repeating image portions often cannot be compressed significantly without a noticeable loss in image quality.
Many video compression standards, including MPEG, MPEG-2, MPEG-4, H.261, and H.263 utilize a block-based Discrete Cosine Transform (DCT) operation on data blocks, 8×8 samples in size. A set of coefficients for each block is generated through the use of a two-dimensional DCT operation. Such coefficients relate to a spatial frequency content of the data block. Subsequently, the 64 DCT coefficients (one for each sample) in a block are uniformly quantized. For H.263, one quantizer step size is applied to every DCT coefficient in a data block and is part of the information that must be transmitted to a H.263 decoder. The quantization process is defined as a division of each DCT coefficient by the quantization step size followed by rounding to the nearest integer. An encoder applies variable uniform quantization to DCT coefficients to reduce the number of bits required to represent them. Compression may be performed on each of the pixels represented by a two-by-two array of blocks containing luminance samples and two blocks of chrominance samples. . This array of six blocks is commonly referred to as a macroblock. The four luminance and two chrominance data blocks in a macroblock combine to represent a 16×16 pixel array.
In an H.263 encoder, variable uniform quantization is applied by means of the quantization parameter that provides quantization step sizes that map the values of DCT coefficients to a smaller set of values called quantization indices. In the H.263 decoder, DCT coefficient recovery is performed, roughly speaking, by multiplying the recovered quantization indices by the inverse quantization step size. The decoder then calculates an inverse DCT using the recovered coefficients.
Although these and other compression methods have proven somewhat effective, there remains a need to improve perceived video quality over low bandwidth transmission channels. Therefore, there is a need for a system and method for static perceptual coding of macroblocks in a video frame.